class_docstr: A subset of the AmazonBook rating dataset from the
  `"LightGCN: Simplifying and Powering Graph Convolution Network for
  Recommendation" <https://arxiv.org/abs/2002.02126>`_ paper.
  This is a heterogeneous dataset consisting of 52,643 users and 91,599 books
  with approximately 2.9 million ratings between them.
  No labels or features are provided.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      transform (callable, optional): A function/transform that takes in an
          :obj:`torch_geometric.data.HeteroData` object and returns a
          transformed version. The data object will be transformed before
          every access. (default: :obj:`None`)
      pre_transform (callable, optional): A function/transform that takes in
          an :obj:`torch_geometric.data.HeteroData` object and returns a
          transformed version. The data object will be transformed before
          being saved to disk. (default: :obj:`None`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
code: def raw_file_names(self) -> List[str]:
      return ["user_list.txt", "item_list.txt", "train.txt", "test.txt"]
  
file_path: pytorch_geometric/torch_geometric/datasets/amazon_book.py
imports: from typing import Callable, List, Optional
  import torch
  from torch_geometric.data import HeteroData, InMemoryDataset, download_url
label_desc: Use image node_id 2 for calling the AmazonBook obj's underlying member method code with example usage: obj.raw_file_names() and returns: list